Enterprise threat detection (ETD) typically collects and stores a large amount/large sets of log data associated with various systems (often referred to as “big data”) associated with an enterprise computing system. The stored data can be analyzed computationally using forensic-type data analysis tools to identify security risks in revealed patterns, trends, interactions, and associations, especially relating to ETD behavior. Appropriate responses can then be taken if anomalous behavior is suspected or identified. Given the amount/size of the stored data and possible multiple attributes or dimensions the stored data, it can be difficult for a user to determine relevant data (or, conversely, filter out unrelated data) when attempting to evaluate an impact of and present an evaluation for a security risk due to vulnerabilities described in ETD security notes.